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arXiv:1912.02777 · Physical Review Applied

Automated Tuning of Double Quantum Dots into Specific Charge States Using Neural Networks

R. Durrer, Benedikt Kratochwil, Jonne Koski, Andreas Landig, Christian Reichl, W. Wegscheider, Thomas Ihn, Eliška Greplová

Published May 8, 2020• 64 citations
arXivHTMLPDF

Abstract

Semiconductor quantum dots are at the forefront of quantum device technology. One longstanding obstacle to scalability is that multidot systems require a lengthy, complex, experimental tuning process. Here the authors introduce a machine-learning-driven algorithm for automated tuning of quantum dots. By letting the algorithm learn from experimental data, they develop a procedure that uses a small set of measurements as its input, and then automatically tunes the double-dot system to the desired charge state. This constitutes a significant step toward fully automated operation of multidot quantum systems.

DOI
10.1103/physrevapplied.13.054019

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Semiconductor quantum dots are at the forefront of quantum device technology. One longstanding obstacle to scalability is that multidot systems require a lengthy, complex, experimental tuning process. Here the authors introduce a machine-learning-driven algorithm for automated tuning of quantum dots. By letting the algorithm learn from experimental data, they develop a procedure that uses a small set of measurements as its input, and then automatically tunes the double-dot system to the desired charge state. This constitutes a significant step toward fully automated operation of multidot quantum systems.
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Abstract This study presents a machine learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This method exploits artificial neural networks to identify noisy transition lines in stability diagrams, guiding a robust exploration strategy leveraging neural network uncertainty estimations. Tested across three distinct offline experimental datasets representing different single-quantum-dot technologies, this approach achieves a tuning success rate of over 99% in optimal cases, where more than 10% of the success is directly attributable to uncertainty exploitation. The challenging constraints of small training sets containing high diagram-to-diagram variability allowed us to evaluate the capabilities and limits of the proposed procedure.
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